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Liquidity and Risk Management Author(s): Nicolae Grleanu and Lasse Heje Pedersen Source: The American Economic Review, Vol. 97, No. 2 (May, 2007), pp. 193-197Published by: American Economic AssociationStable URL: http://www.jstor.org/stable/30034445Accessed: 20-03-2015 12:33 UTC

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SEARCH-AND-MATCHING FINANCIAL MARKETSt

Liquidity and Risk Management By NICOLAE G^XRLEANU AND LASSE HEJE PEDERSEN*

This paper provides a model of the interaction between risk-management practices and market liquidity. Our main finding is that a feedback effect can arise. Tighter risk management leads to market illiquidity, and this illiquidity further tightens risk management.

Risk management plays a central role in insti- tutional investors' allocation of capital to trad- ing. For instance, a risk manager may limit a trading desk's one-day 99 percent value at risk (VaR) to $1 million. This means that the trad- ing desk must choose a position such that, over the following day, its value drops no more than $1 million with 99 percent probability. Risk management helps control an institution's use of capital while limiting default risk, and helps mitigate agency problems. Phillipe Jorion (2000, xxiii) states that VaR "is now increasingly used to allocate capital across traders, business units, products, and even to the whole institution."

We do not focus on the benefits of risk man- agement within an institution adopting such con- trols, but, rather, on the aggregate effects of such practices on liquidity and asset prices. An institu- tion may benefit from tightening its risk manage- ment and restricting its security position, but as a consequence it cannot provide as much liquidity to others. We show that, if everyone uses a tight risk management, then market liquidity is low- ered in that it takes longer to find a buyer with unused risk-bearing capacity, and, since liquidity is priced, prices fall.

Not only does risk management affect liquid- ity; liquidity can also affect risk-management practices. For instance, the Bank for International Settlements (2001, 15) states, "For the internal risk management, a number of institutions are exploring the use of liquidity adjusted-VaR, in which the holding periods in the risk assessment are adjusted to account for market liquidity, in particular by the length of time required to unwind positions." For instance, if liquidation is expected to take two days, a two-day VaR might be used instead of a one-day VaR. Since a secu- rity's risk over two days is greater than over one day, this means a trader must choose a smaller position to satisfy his liquidity-adjusted value at risk (LVaR) constraint. One motivation for this constraint is that, if an institution needs to sell, its maximum loss before the completion of the sale is limited by the LVaR.

The main result of the paper is that subjecting traders to an LVaR gives rise to a multiplier effect. Tighter risk management leads to more restricted positions, hence longer expected selling times, implying higher risk over the expected selling period, which further tightens the risk manage- ment, and so on. This feedback between liquidity and risk management can help explain why liquid- ity can suddenly drop. We show that this "snow- balling" illiquidity can arise if volatility rises, or if more agents face reduced risk-bearing capac- ity-for instance, because of investor redemp- tions, losses, or increased risk aversion.

Our link between liquidity and risk manage- ment is a testable prediction. While no formal empirical evidence is available, to our knowl- edge, our prediction is consistent with anecdotal evidence on financial market crises. For exam- ple, in August 1998 several traders lost money due to a default of Russian bonds and, simulta- neously, market volatility increased. As a result, the (L)VaR of many investment banks and other institutions increased. To bring risk back in line, many investment banks reportedly asked traders to reduce positions, leading to falling prices and

tDiscussants: Dimitri Vayanos, London School of Economics; Neil Wallace, Pennsylvania State University; Manuel Amador, Stanford University.

* Gdrleanu: Wharton School, University of Pennsylva- nia, 3620 Locust Walk, Philadelphia, PA 19104-6367, and National Bureau of Economic Research, and Centre for Economic Policy Research (e-mail: garleanu@wharton. upenn.edu); Pedersen: Stern School of Business, New York University, 44 West Fourth Street, Suite 9-190, New York, NY 10012-1126, NBER, and CEPR (e-mail: lpederse@ stern.nyu.edu). We are grateful for helpful conversations with Franklin Allen, Dimitri Vayanos, and Jeff Wurgler.

193

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194 AEA PAPERS AND PROCEEDINGS MAY 2007

lower liquidity. These market moves exacerbated the risk-management problems, fueling the crisis in a similar manner to the one modeled here.

We capture these effects by extending the search model for financial securities of Darrell Duffie, Garleanu, and Pedersen (2005, forthcom- ing, henceforth DGP). This framework of time- consuming search is well suited for modeling liquidity-based risk management as it provides a natural framework for studying endogenous sell- ing times. While DGP relied on exogenous posi- tion limits, we endogenize positions based on a risk-management constraint, and consider both a simple and a liquidity-adjusted VaR. Hence, we solve the fixed-point problem of jointly calculat- ing endogenous positions given the risk-manage- ment constraint and computing the equilibrium (L)VaR given the endogenous positions that deter- mine selling times and price volatility. Pierre- Olivier Weill (forthcoming) considers another extension of DGP in which market maker liquid- ity provision is limited by capital constraints. Our multiplier effect is similar to that of Markus K. Brunnermeier and Pedersen (2006) who show that liquidity and traders' margin requirements can be mutually reinforcing.

I. Model

The economy has two securities: a "liquid" security with risk-free return r (i.e., a "money- market account"), and a risky illiquid security. The risky security has a dividend-rate process X and a price P(X), which is determined in equi- librium. The dividend rate is L6vy with finite variance. It has a constant drift normalized to zero, E, (X(t + T) - X(t)) = 0, and a volatility ax > 0, i.e.,

(1) var, (X(t + T) - X(t)) = a2x T.

Examples include Brownian motions, (com- pound) Poisson processes, and sums of these.

The economy is populated by a continuum of agents who are risk neutral and infinitely lived, have a time-preference rate equal to the risk-free interest rate r > 0, and must keep their wealth bounded from below. Each agent is characterized by an intrinsic type i E {h, 1}, which is a Markov chain, independent across agents, and switching from 1 ("low") to h ("high") with intensity A,, and back with intensity Ad. An agent of type i

holding 8, shares of the asset incurs a holding cost of 8 > 0 per share and per unit of time if he violates his risk-management constraint

(2) vart (Ot [P(Xt+,) - P(Xt)]) (o'i)2 where a' is the risk-bearing capacity, defined by o"h = &"> 0 and o-T = 0. The low risk-bearing capacity of the low-type agents can be inter- preted as a need for more stable earnings, hedg- ing reasons to reduce a position, high financing costs, or a need for cash (e.g., an asset manager whose investors redeem capital).'

We use this constraint as a parsimonious way of capturing risk constraints, such as the very popular VaR constraint,2 which are used by most financial institutions. Our results are robust in that they rely on two natural proper- ties of the measure of risk: the risk measure increases with the size of the security position, and the length of the time period r over which the risk is assessed. While the constraint is not endogenized in the model, we note that its wide use in the financial world is probably due to agency problems, default risk, and the need to allocate scarce capital.

We consider two types of risk management: (a) "simple risk management," in which the vari- ance of the position in (2) is computed over a fixed time horizon r; and (b) "liquidity-adjusted risk management," in which the variance is computed over the time required for selling the asset to an unconstrained buyer, which will be a random equilibrium quantity.

Because agents are risk neutral and we are interested in a steady-state equilibrium, we restrict attention to equilibria in which, at any given time and state of the world, an agent holds either 0 or 0 units of the asset, where 0 is the largest

1 An interesting extension of our model would consider the direct benefit of tighter risk management, which could be captured by a lower Ad.

2 A VaR constraint stipulates that Pr(-O[P(X,,,) - P(X,)]

-

VaR) < :7r for some risk limit VaR and some con- fidence level 7r. If X is a Brownian motion, this is the same as (2). We note that rather than considering only price risk, we could alternatively consider the risk of the gains process (i.e., including dividend risk) G,,, = P(X(t + 7)) - P(X(t)) + fX(s) ds. This yields qualitatively similar results (and quantitatively similar for many reasonable parameters since dividend risk is orders of magnitude smaller than price risk over a small time period).

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VOL. 97 NO. 2 LIQUIDITY AND RISK MANAGEMENT 195

position that satisfies (2) with o"' = 6 taking the prices and search times as given.3 Hence, the set of agent types is T = {ho, hn, lo, ln}, with the letters "h" and "'" designating the agent's current intrinsic risk-bearing state as high or low, respec- tively, and with "o" or "n" indicating whether the agent currently owns 0 shares or none, respec- tively. We let ug(t) denote the fraction at time t of agents of type G E T . These fractions add up to 1 and markets must clear:

(3) 1 = tho hn+ ,Llo

+ ln,

(4) 0 = 0(-ho + + lto), where 0 > 0 is the total supply of shares per investor.

Central to our analysis is the notion that the risky security is not perfectly liquid, in the sense that an agent can trade it only when she finds a counterparty. Every agent finds a potential counterparty, selected randomly from the set of all agents, with intensity A, where A > 0 is an exogenous parameter characterizing the mar- ket liquidity for the asset. Hence, the intensity of finding a type-f investor is A/zt,

that is, the search intensity multiplied by the fraction of investors of that type. When two agents meet, they bargain over the price, with the seller hav- ing bargaining power q E [0, 1].

This model of illiquidity directly captures the search that characterizes over-the-counter (OTC) markets. In these markets, traders must find an appropriate counterparty, which can be time consuming. Trading delays also arise due to time spent gathering information, reach- ing trading decisions, mobilizing capital, etc. Hence, trading delays are commonplace, and, therefore, the model can also capture features of other markets such as specialist and electronic limit-order-book markets, although these mar- kets are, of course, distinct from OTC markets.

II. Equilibrium Risk Management, Liquidity, and Prices

We now proceed to derive the steady-state equilibrium agent fractions Ax, the maximum-

holding 0, and the price P. Naturally, low-type owners lo want to sell and high-type non-owners hn want to buy, which leads to

(5) 0 = -2Ap.Lhn(t)p.Llo(t) -Auplo(t) + Ad ho(t)

and three more such steady-state equations. Equation (5) states that the change in the fraction of lo agents has three components, correspond- ing to the three terms on the right-hand-side of the equation. First, whenever a lo agent meets a hn investor, he sells his asset and is no longer a lo agent. Second, whenever the intrinsic type of a lo agent switches to high, he becomes a ho agent. Third, ho agents can switch type and become lo. Duffie, Garleanu, and Pedersen (2005) show that, taking 0 as fixed, there is a unique stable steady- state mass distribution as long as 06 0. Here, agents' positions 0 are endogenous and depend on 1a, so that we must calculate a fixed point.

Agents take the steady-state distribution g as fixed when they derive their optimal strategies and utilities for remaining lifetime consumption, as well as the bargained price P. The utility of an agent depends on his current type ((t) E T (i.e., whether he is a high or a low type and whether he owns zero or 0 shares), the current dividend X(t), and the wealth W(t) in his bank account:

(6) V,(X(t), W,) = Wt + 1(~E{ho,,lo})X(t)/r+ovy, where the type-dependent utility coefficients vg are to be determined. With q the bargaining power of the seller, bilateral Nash bargaining yields the price

(7) PO = (Vlo - Vt) (1 - q) + (Vho - Vhn q. We conjecture, and later confirm, that the equi- librium asset price per share is of the form

(8) P(X(t)) = X(t)/r + p, for a constant p to be determined. The value- function coefficients vy andp are given by a set of Hamilton-Jacobi-Bellman equations, stated and solved in the Appendix available at www.e-aer. org/data/may07/p07048_app.pdf. The Appendix contains all the proofs.

PROPOSITION 1: If the risk-limit &r is suffi- ciently large, there exists an equilibrium with

3 Note that the existence of such an equilibrium requires that the risk limit & not be too small relative to the total sup- ply 0, a condition that we assume throughout.

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196 AEA PAPERS AND PROCEEDINGS MAY 2007

holdings 0 and & that satisfy the risk manage- ment constraint (2) with equality for low- and high-type agents, respectively. With simple risk management, the equilibrium is unique and

r- 1 (9) =

-x \/

With liquidity-adjusted risk management, 0 depends on the equilibrium fraction of potential buyers AIhn and satisfies

(10) = r 2 hn

In both cases, the equilibrium price is given by

X, (11) P(Xt) = r

S r(1 - q) + Ad + 2AI/.o(1 - q)

r r + Ad + 2Al.ro(1 - q) + Au + 2A-hnq'

where the fractions of agents gt depend on the type of risk management.

These results are intuitive. The "position limit" 8 increases in the risk limit &- and decreases in the asset volatility and in the square root of the VaR period length, which is r under simple risk management and

(2Ahhn)-1 under liquid-

ity-adjusted risk management. In the latter case, position limits increase in the search intensity and in the fraction of eligible buyers I-hn. The price equals the present value of divi- dends, Xt/r, minus a discount for illiquidity. Naturally, the liquidity discount is larger if there are more low-type owners in equilibrium (-tlo is larger) and fewer high-type nonowners ready to buy (Ihn is smaller).

Of the equilibria with liquidity-adjusted risk management, we concentrate on the ones that are stable, in the sense that increasing 0 marginally would result in equilibrium quantities violating the VaR constraint (2). Conversely, an equilib- rium is unstable if a marginal change in hold- ings that violates the constraint would result in

the equilibrium adjusting so that the constraint is not violated. If an equilibrium exists, then a stable equilibrium exists. Indeed, the equilib- rium with the largest 0 is stable and has the high- est welfare among all equilibria.

The main result of the paper characterizes the equilibrium connection between liquidity and risk management.

PROPOSITION 2: Suppose that &" is large enough for the existence of an equilibrium. Consider a stable equilibrium with liquidity- adjusted risk management and let 7 = 1(2AUhn), which means that the equilibrium allocations and price are the same with simple risk man- agement. Consider any combination of the conditions (a) higher dividend volatility orx, (b) lower risk limit C; (c) lower meeting intensity A, (d) lower switching intensity Au to the high risk- bearing state, and (e) higher switching intensity Ad to the low risk-bearing state. Then, (i) the equilibrium position 0 decreases, (ii) expected search times for selling increase, and (iii) prices decrease. All three effects are larger with liquid- ity-adjusted risk management.

To see the intuition for these results, consider the impact of a higher dividend volatility. This makes the risk-management constraint tighter, inducing agents to reduce their positions and spreading securities among more agents, thus leaving a smaller fraction of agents with unused risk-bearing capacity. Hence, sellers' search time increases and their bargaining position worsens, leading to lower prices. This price drop is due to illiquidity, as agents are risk neutral.4

With liquidity-adjusted risk management, the increased search time for sellers means that the risk over the expected liquidation period rises, thus further tightening the risk-management constraint, reducing positions, increasing search times, and so on.

This multiplier also increases the sensitiv- ity of the economy with liquidity-adjusted risk management to the other shocks (b)-(e). Indeed, a lower risk limit (b) is equivalent to a higher

4 In a Walrasian market with immediate trade, the price is the present value of dividends X/r when (0/0) < Ah/ (Au + Ad), a condition that is satisfied in our examples below. (When /0 > A,/(A, + Ad), the Walrasian price is (X-6)/r .)

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VOL. 97 NO. 2 LIQUIDITY AND RISK MANAGEMENT 197

Expected

sale

times

(years)

0.015

0.014

0.013

0.012

0.011

0.01

0.009

0.008

0.007

Liquidity VaR Simple VaR

0.2 0.25 0.3 0.35 Dividend volatility ox

Price

9.9

9.88

9.86

9.84

9.82

9.8

9.78

- Liquidity VaR ' Simple VaR

0.2 0.25 0.3 0.35 0.4 Dividend volatility ox

FIGURE 1

Note: The effects of dividend volatility on equilibrium seller search times (left panel) and prices (right panel) with simple (dashed line) and liquidity-adjusted (solid line) risk management, respectively.

dividend risk. The "liquidity shocks" (c)-(e) do not affect the equilibrium position 0 with simple risk management, but they do increase the sell- ers' search times and reduce prices. With liquid- ity-adjusted risk management, these liquidity shocks reduce security positions, too, because of increased search times and, as explained above, a multiplier effect arises.

The multiplier arising from the feedback between trading liquidity and risk manage- ment clearly magnifies the effects of changes in the economic environment on liquidity and prices. Our steady-state model illustrates this point using comparative static analyses that essentially compare across economies. Similar results would arise in the time series of a single economy if there were random variation in the model characteristic, e.g., parameters switched in a Markov chain as in Duffie, Garleanu, and Pedersen (forthcoming). In the context of such time-series variation, our multiplier effect can generate the abrupt changes in prices and selling times that characterize crises.

We illustrate our model with a numerical example in which A = 100, r = 0.1, Xo - 1, Ad = 0.2, Au = 2, 8 = 3, q = 0.5, 0 = 1, and S= 1. Figure 1 shows how prices (right panel) and sellers' expected search times (left panel) depend on asset volatility. The solid line shows this for liquidity-adjusted risk management and the dashed line for simple risk management

with 7 = 0.0086, which is chosen so that the risk management schemes are identical for ox = 0.3. Search times increase and prices decrease with volatility. These sensitivities are stron- ger (i.e., the curves are steeper) with liquidity- adjusted risk management due to the interaction between market liquidity (i.e., search times) and risk management.

REFERENCES

Brunnermeier, Markus K., and Lasse Heje Pedersen. 2006. "Market Liquidity and Fund- ing Liquidity." Unpublished.

Duffie, Darrell, Nicolae Girleanu, and Lasse Heje Pedersen. 2005. "Over-the-Counter Markets." Econometrica, 73(6): 1815-47.

Duffie, Darrell, Nicolae Girleanu, and Lasse Heje Pedersen. Forthcoming. "Valuation in Over- the-Counter Markets." Review of Financial Studies.

Bank for International Settlements. 2001. "Final Report of the Multidisciplinary Working Group on Enhanced Disclosure." http://www. bis.org/publ/joint0l.htm.

Jorion, Phillipe. 2000. Value at Risk. New York: McGraw-Hill.

Weill, Pierre-Olivier. Forthcoming. "Leaning against the Wind." Review of Economic Studies.

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Article Contentsp. 193p. 194p. 195p. 196p. 197

Issue Table of ContentsThe American Economic Review, Vol. 97, No. 2 (May, 2007), pp. i-ix, 1-591Front MatterEditors' Introduction [p. viii-viii]Foreword [p. ix-ix]Richard T. Ely LectureBeliefs, Doubts and Learning: Valuing Macroeconomic Risk [pp. 1-30]

The Economic of Human DevelopmentThe Technology of Skill Formation [pp. 31-47]

Model Validation and Model ComparisonEx Ante Policy Evaluation, Structural Estimation, and Model Selection [pp. 48-52]Testing the Mechanisms of Structural Models: The Case of the Mickey Mantle Effect [pp. 53-59]Bayesian Model Comparison and Validation [pp. 60-64]

Risk Sharing and Cooperation in Social NetworksReciprocity in Groups and the Limits to Social Capital [pp. 65-69]Risk Sharing across Communities [pp. 70-74]Risk Sharing and Network Formation [pp. 75-79]Community Size and Network Closure [pp. 80-85]

Networked InteractionsCommunication Networks: Knowledge and Decisions [pp. 86-91]Diffusion of Behavior and Equilibrium Properties in Network Games [pp. 92-98]Financial Networks [pp. 99-103]

Social Insurance Programs: Good for Workers? Good for the Labor Market?Evaluating the Worker Profiling and Reemployment Services System Using a Regression Discontinuity Approach [pp. 104-107]Unemployment Benefits, Unemployment Duration, and Post-Unemployment Jobs: A Regression Discontinuity Approach [pp. 108-112]The Spike at Benefit Exhaustion: Leaving the Unemployment System or Starting a New Job? [pp. 113-118]Distinguishing Income from Substitution Effects in Disability Insurance [pp. 119-124]

Developments in Dynamic Mechanism DesignAn Ascending Auctions for Independent Values: Uniqueness and Robustness to Strategic Uncertainty [pp. 125-130]Designing Efficient Mechanisms for Dynamic Bilateral Trading Games [pp. 131-136]On Quitting Rights in Mechanism Design [pp. 137-141]

Decision Theory: New Methods, New InsightsNeuroeconomic Studies of Impulsivity: Now or Just as Soon as Possible? [pp. 142-147]The Neuroeconomic Theory of Learning [pp. 148-152]Revealing Preferences Graphically: An Old Method Gets a New Tool Kit [pp. 153-158]

Beliefs in the Utility FunctionOptimal Beliefs, Asset Prices, and the Preference for Skewed Returns [pp. 159-165]Experimental Testing of Intrinsic Preferences for Nonlnstrumental Information [pp. 166-169]Guilt in Games [pp. 170-176]

Contracts and FairnessAdding a Stick to the Carrot? The Interaction of Bonuses and Fines [pp. 177-181]Incomplete Contracts and Ownership: Some New Thoughts [pp. 182-186]Can Contract Theory Explain Social Preferences? [pp. 187-192]

Search-and-Matching Financial MarketsLiquidity and Risk Management [pp. 193-197]Search in Asset Markets: Market Structure, Liquidity, and Welfare [pp. 198-202]Information Percolation in Large Markets [pp. 203-209]

Capital Market FrictionsMarket Maker Inventories and Stock Prices [pp. 210-214]Slow Moving Capital [pp. 215-220]Systemic Illiquidity in the Federal Funds Market [pp. 221-225]

Habit Persistence and the MacroeconomyIncreasing Income Inequality, External Habits, and Self-Reported Happiness [pp. 226-231]Pricing to Habits and the Law of One Price [pp. 232-238]Explaining Asset Prices with External Habits and Wage Rigidities in a DSGE Model [pp. 239-243]

Inaction and Adjustment: Consequences for Households and FirmsOptimal Inattention to the Stock Market [pp. 244-249]Uncertainty and the Dynamics of R&D [pp. 250-255]Investment under Uncertainty with Strategic Debt Service [pp. 256-261]

Monetary Systems: Transitions and ExperimentsThe Bank of Amsterdam and the Leap to Central Bank Money [pp. 262-265]Backing, the Quantity Theory, and the Transition to the US Dollar, 1723-1850 [pp. 266-270]The Political Economy of the US Monetary Union: The Civil War Era as a Watershed [pp. 271-275]John Law's System [pp. 276-279]

Wars, Finance, and War FinanceThe Net Worth of the US Federal Government, 1784-1802 [pp. 280-284]The Great Financial Crisis of 1914: What Can We Learn from Aldrich-Vreeland Emergency Currency? [pp. 285-289]The McKenna Rule and UK World War I Finance [pp. 290-294]How Occupied France Financed Its Own Exploitation in World War II [pp. 295-299]

The Transparency of Political InstitutionsThe Perils of Transparency in Bureaucracies [pp. 300-305]Decision-Making Procedures for Committees of Careerist Experts [pp. 306-310]The Transparency of Politics and the Quality of Politicians [pp. 311-315]

Is Foreign Aid Helping?Aid Effectiveness: Opening the Black Box [pp. 316-321]Does Aid Affect Governance? [pp. 322-327]Was Development Assistance a Mistake? [pp. 328-332]

Exchange Rate PuzzlesThe Returns to Currency Speculation in Emerging Markets [pp. 333-338]If Exchange Rates Are Random Walks, Then Almost Everything We Say about Monetary Policy Is Wrong [pp. 339-345]Random Walk Expectations and the Forward Discount Puzzle [pp. 346-350]

New Approaches to International TradeUnbalanced Trade [pp. 351-355]Trade Flow Dynamics with Heterogeneous Firms [pp. 356-361]Pricing-to-Market in a Ricardian Model of International Trade [pp. 362-367]

Globalization and Economic Outcomes for Minority GroupsThe Effect of Globalization on the Performance of Small-and Medium-Sized Enterprises in the United States: Does Owners' Race/Ethnicity Matter? [pp. 368-372]The Effects of Recent Immigration on Racial/Ethnic Labor Market Differentials [pp. 373-377]Inward Foreign Direct Investment and Racial Employment Patterns in US Manufacturing [pp. 378-382]Differential Impacts of Immigrants on Native Black and White Workers [pp. 383-387]

Growth, Education, and Investment in ChildrenNonlinearities and Robustness in Growth Regressions [pp. 388-392]Public Education Expenditures, Taxation, and Growth: Linking Data to Theory [pp. 393-397]Why Do Poor Children Lose Health Insurance in the SCHIP Era? The Role of Family Health [pp. 398-401]The Effect of Child Gender on Parents' Labor Supply: An Examination of Natives, Immigrants, and their Children [pp. 402-406]

Gender and Labor Market OutcomesReaching Equilibrium in the Market for Obstetricians and Gynecologists [pp. 407-411]Gender Differences in the Labor Market: Impact of IRCA's Amnesty Provisions [pp. 412-416]The Role of Labor Market Intermittency in Explaining Gender Wage Differentials [pp. 417-421]Women Helping Women, Men Helping Women? Same-Gender Mentoring, Initial Job Placements, and Early Career Publishing Success for Economics PhDs [pp. 422-426]

Medical Innovations and the Social Value of Health ProgressIntegrated Insurance Design in the Presence of Multiple Medical Technologies [pp. 427-432]Social Value and the Speed of Innovation [pp. 433-437]The Impact of New Drugs on US Longevity and Medical Expenditure, 1990-2003: Evidence from Longitudinal, Disease-Level Data [pp. 438-443]

Emperical Industrial OrganizationIdentification and Estimation of Bidders' Risk Aversion in First-Price Auctions [pp. 444-448]An Estimable Dynamic Model of Entry, Exit, and Growth in Oligopoly Retail Markets [pp. 449-454]Bounding Revenue Comparisons across Multi-Unit Auction Formats under -Best Response [pp. 455-458]Linear Regression Estimation of Discrete Choice Models with Nonparametric Distributions of Random Coefficients [pp. 459-463]

Behavioral Welfare EconomicsToward Choice-Theoretic Foundations for Behavioral Welfare Economics [pp. 464-470]Welfare without Happiness [pp. 471-476]Mistakes in Choice-Based Welfare Analysis [pp. 477-481]

Biological Evolution and EconomicsSome Evolutionary Economics of Family Partnerships [pp. 482-486]Habits, Peers, and Happiness: An Evolutionary Perspective [pp. 487-491]Why Do We Die? Economics, Biology, and Aging [pp. 492-495]The Evolution of Intertemporal Preferences [pp. 496-500]

The Market and Pre-Market for Graduate Students in EconomicsIs There an Insider Advantage in Getting Tenure? [pp. 501-505]The Search for Economics Talent: Doctoral Completion and Research Productivity [pp. 506-511]What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes [pp. 512-518]

Proceedings of the One Hundred Nineteenth Annual Meeting [pp. 519-591]Back Matter